{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](https://raw.githubusercontent.com/Qinbf/tf-model-zoo/master/README_IMG/01.jpg)\n",
    "AI MOOC： **www.ai-xlab.com**  \n",
    "如果你也是AI爱好者，可以添加我的微信一起交流：**sdxxqbf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UsG2/n02rqRiRKuFexJeeC962VmMb5J7H236DNZ2+4X3nXsIXTyqdEkURCnPwxf+B4T/E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb34b4048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建40个点\n",
    "x_data = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]\n",
    "y_data = [0]*20 +[1]*20\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit the model\n",
    "model = svm.SVC(kernel='linear')\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70945789, 0.77571142]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.49284762])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T/G1GR/OI8PBwJkyYwJYtWwgNDaVhw4Z06NCBs2fPGh1NZEPuOGOvDPystf5V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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb34a7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取分离平面 \n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "x_test = np.array([[-5],[5]])\n",
    "d = -model.intercept_/model.coef_[0][1]\n",
    "k = -model.coef_[0][0]/model.coef_[0][1]\n",
    "y_test = d + k*x_test\n",
    "plt.plot(x_test, y_test, 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.04395816, -0.6939937 ],\n",
       "       [ 1.32797658,  0.70993425]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画出通过支持向量的分界线\n",
    "b1 = model.support_vectors_[0]\n",
    "y_down = k*x_test + (b1[1] - k*b1[0])\n",
    "b2 = model.support_vectors_[-1]\n",
    "y_up = k*x_test + (b2[1] - k*b2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fVAYl3bxipJS7pJRCSukrpSx39/ZIUdekMrlzqz9v3VK+NMOGqTP148aZrpKm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\nRIyKXUQkYv4PSnWA6zE1EHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb6555940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "x_test = np.array([[-5],[5]])\n",
    "d = -model.intercept_/model.coef_[0][1]\n",
    "k = -model.coef_[0][0]/model.coef_[0][1]\n",
    "y_test = d + k*x_test\n",
    "plt.plot(x_test, y_test, 'k')\n",
    "plt.plot(x_test, y_down, 'r--')\n",
    "plt.plot(x_test, y_up, 'b--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
